A Semi-Automated Tracking System for Monitoring Treatment with Novel Anti-amyloid Therapies
Daniel Saukkonen1, Mansi Karwa1, Diane Maimonis2, John Dickson3, Liliana Ramirez-Gomez3, Kirk Daffner4, Maria Teresa Gomez-Isla3, Michael Erkkinen1
1Neurology, Brigham and Women's Hospital, 2Brigham and Women's Hospital, 3Neurology, Massachusetts General Hospital, 4Brigham & Women's Hospital - Harvard Medical School
Objective:

To present the design and implementation of a clinical tracking system for patients receiving lecanemab.

Background:

Approval of anti-amyloid therapies for Alzheimer’s disease has spurred new clinical programs to ensure safe and efficient treatment administration. Safe treatment necessitates MRI scans at specific infusion-dependent intervals to screen for amyloid-related imaging abnormalities (ARIA). Oversight of scheduling and timing of infusions, MRIs, and clinic visits is essential to ensure appropriately timed infusions but poses logistical challenges when each component is scheduled and managed by different decentralized teams. Manual tracking approaches are cumbersome and may fail with large patient panels or understaffed clinics.

Design/Methods:

We developed a tracking system using electronic health record (EHR)-based reports to capture and organize essential data related to infusions, MRIs, and other clinical features. We export this data to secure external programs, where algorithms identify patients undergoing infusions and MRIs and those needing additional orders, appointment adjustments, phone screenings, and other clinical-administrative tasks, flagging them for timely action. The clinical and administrative teams use this organized information to prioritize tasks, which are manually cross-verified for accuracy.

Results:

This clinical tracking system, implemented in a multi-campus academic hospital system, supports the care of over 250 lecanemab patients by generating weekly lists for infusions, MRIs, and phone calls while monitoring ARIA cases. These programs function as intended to deliver these outputs, facilitating a comprehensive view of each patient’s clinic appointments and infusion schedules, and identifying time-sensitive tasks across a decentralized hospital network. Limitations include delays in real-time EHR updates and exporting data outside the EHR.

Conclusions:
The use of EHR-based reports with algorithmic logic can support oversight and tracking of patients on novel anti-amyloid therapies. The scalability of semi-automated systems may address safety concerns with manual tracking of larger patient panels. This approach may have applications for other neurologic therapeutics requiring complex patient monitoring.
10.1212/WNL.0000000000211308
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